In collaboration with Merrick Howarth and Bella Raja

Fighting climate change not only means setting ambitious targets, but matching those ambitions with the ground work. San Jose is a city on a mission — become carbon neutral by 2030. This catchy phrase means that the city will offset all emissions it produces. To do this adequately and accurately, the city must have a reliable way to measure emissions and understand the both the underlying factors that contribute to GHG emissions, and how best to develop plans that not only allocates political responsibility, but holds the right entities accountable. Ultimately, the transboundary nature of greenhouse gas emissions makes this an epidemiological challenge for the politically bounded world we live in. The report below attempts at a high level to define sustainability for downtown San Jose and share insights.

Methodology

We examined emissions from two sources: vehicles and buildings, between 2013 and 2019. We chose this time frame due to availability of PG&E energy usage data. We also conducted these analyses at the ZIP code level, since PG&E data is collected at this scale. Our chosen ZIP codes were 95113 and 95112, which can be visualized below.

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Vehicle Emissions

We used vehicle emissions as a proxy for the transportation sector as a whole, with the primary assumption being that the majority of trips are commutes to work. Using commute data from LEHD Origin-Destination Employment Statistics (LODES), we were able to route commute trips originating from our chosen ZIP codes. The map below shows work trips that occured at least 261 times between 2013 and 2019.

With these trips, we applied emissions rate information provided by the California Air Resources Board to our commute routes using the following equation for each year:

\(\text{GHG Emissions} = \text{VMT}*mtCO_2{Running Exhaust} + [2*\text{trips}*mtCO_2StartExhaust]\)

VMT, which stands for vehicle miles traveled, is measured by commute trips distance summed by year. Trips are the number of trips made per year. \(CO_2\) Running Exhaust and Start Exhaust are \(CO_2\) emissions rate associated with driving and starting cars. This data from EMFAC also contains an estimated breakdown of the type of vehicles on the road. Note that we multiply by two to indicate round trips.

The plots below show annual and average vehicle emissions:

Building Emissions

We used PG&E electricity and gas data to estimate energy usage and their associated emissions. This data comes in as kilowatt hours (kWh) and therms so we eventually convert these to emissions in terms of metric tonnes of \(CO_2\)-equivalent. We then normalized commercial and residential energy usage by jobs and population respectively. Again, the job data came from LODES — the same data set use to plot commute routes. The population data came from ACS census data. This gives us an idea of how much energy usage per capita. We can further normalize this by heating and cooling degree days, which are measures of temperature net exceedance above or below thresholds. We set our threshold at 65 deg F. This analysis can be be visualized in the plot below:

Results + Insights

Vehicle and building emissions only comprise a portion of total emissions produced in San Jose, however they still give us insight to how the city is performing and how it can be improved to reach its carbon-neutral goals by 2030.

The first plot below shows total annual emissions broken down by end-use category.

Here, we see that emissions have been decreasing every year — not only is this great news, but it’s indicative of active measures to reduce emissions, especially since population has increased during this time.

We can visualize the proportions of each end-use category in the plot below:

Here, we can see that 96% of emissions comes from buildings and the remaining 4% comes from vehicles. Remember that our vehicle emissions only had commute trips, whereas in reality we make trips for several other purposes. Despite this, we can see that vehicle emissions increased from 4% to 7% of emissions between 2013 and 2019. You can hover each section to visualize its proportion. Within our building emissions, we see that the bulk of emisssions come from electric commercial buildings. Likewise, with residential buildings, most of the emissions (albiet by a 1% difference) came from buildings with primarily electric energy sources. This is a bit confusing since we are pushing for electrifiying everything. There might be an outside factor contributing to this dissonance. If it is accurate, it might speak to us decarbonizing the electric grid itself because there’s no use electrifying everything if the grid still draws on emission-heavy sources. One major discrepancy can be in 2019 where electric commericial buildings emissions decreased to less than 1%. Unless, San Jose experience a massive commercial exodus (with an economic collapse ensuing), there’s a chance there’s a mistake with the data. One other explanation for this might be ay changes that occur with out data is accounted and reported. If so, San Jose must update this process for all other sectors so that it can accurately track emissions.

Emissions Allocation

Central to this discussion about GHG emissions in San Jose is the larger idea about how best to account for these emissions. As I stated earlier, emissions are transboundary, however our society and political structure isn’t. San Jose is a city where people live and businesses operate. Some people drive through the city and others’ activity space resides entirely within the city. If the city (and the Bay Area) wants to reach its carbon goals, it must have some way of accounting and controlling these emissions. One way to do that is to consider the emissions-inducing potential of new developments, especially those that can significantly impact GHG-inducing activities. For example, if Amazon or any other tech company were to relocate to San Jose, thereby inducing traffic or resident influx with their individual trips, they must be held responsible with taxes. While taxes don’t exactly prevent emissions, they are a potential tool cities can use to control growth and development in their city. If companies are willing to pay taxes, then that will result in revenue for the city to use in other areas. This could look like funding rebate programs to encourage electric vehicle adoption or retrofits to homes, schools, or other public utilties to make them more energy-efficient.

Ultimately this vision leans more heavily into political boundaries. Everything within the city’s jurisdiction, the city can set rules to manage emissions. Another application of this idea would be implementing cordon pricing to minimize vehicle emissions or even decintivizing people from driving through certain areas at certain times. Of course all of these systems will need to have a control and management system.